Congestion and centrality in traffic flow on complex networks

The central points of communication network flow has often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), which non-central vertices getting a comparatively large portion of the traffic.Comment: To appear in Advances in Complex System

Emotion Recognition and Traffic-Related Risk-Taking Behavior in Patients with Neurodegenerative Diseases

Objectives : Neurodegenerative diseases (NDDs), such as Alzheimer's disease, frontotemporal dementia, dementia with Lewy bodies, and Huntington's disease, inevitably lead to impairments in higher-order cognitive functions, including the perception of emotional cues and decision-making behavior. Such impairments are likely to cause risky daily life behavior, for instance, in traffic. Impaired recognition of emotional expressions, such as fear, is considered a marker of impaired experience of emotions. Lower fear experience can, in turn, be related to risk-taking behavior. The aim of our study was to investigate whether impaired emotion recognition in patients with NDD is indeed related to unsafe decision-making in risky everyday life situations, which has not been investigated yet.  Methods: Fifty-one patients with an NDD were included. Emotion recognition was measured with the Facial Expressions of Emotions: Stimuli and Test (FEEST). Risk-taking behavior was measured with driving simulator scenarios and the Action Selection Test (AST). Data from matched healthy controls were used: FEEST (n = 182), AST (n = 36), and driving simulator (n = 18).   Results: Compared to healthy controls, patients showed significantly worse emotion recognition, particularly of anger, disgust, fear, and sadness. Furthermore, patients took significantly more risks in the driving simulator rides and the AST. Only poor recognition of fear was related to a higher amount of risky decisions in situations involving a direct danger.   Conclusions: To determine whether patients with an NDD are still fit to drive, it is crucial to assess their ability to make safe decisions. Measuring emotion recognition may be a valuable contribution to this judgment

Methotrexate area under the curve is an important outcome predictor in patients with primary CNS lymphoma: A pharmacokinetic–pharmacodynamic analysis from the IELSG no. 20 trial

This analysis was initiated to define the predictive value of the area under the curve of high-dose methotrexate (AUC(HD-MTX)) in patients with primary central nervous system lymphoma (PCNSL).; We included 55 patients with PCNSL and available pharmacokinetic (PK) data from the International Extranodal Lymphoma Study Group (IELSG) no. 20 trial, randomised to HD-MTX (n=30) or HD-MTX and high-dose cytarabine (HD-AraC) (n=25). Individual AUC(HD-MTX) from population PK analysis was tested on drug toxicity and clinical outcome using multivariate logistic regression analysis and Cox hazards modelling.; AUC(HD-MTX), the IELSG score and treatment group were significant predictors for treatment response (complete or partial) in the adjusted model. The AUC(HD-MTX) did not predict toxicity, with the exception of liver toxicity and neutropaenia. A high AUC(HD-MTX) was associated with better event-free survival (EFS) (P=0.01) and overall survival (OAS) (P=0.02). Both the AUC(HD-MTX) and the IELSG score were significant predictors of EFS and OAS in the adjusted model, with a hazard ratio of 0.82 and 0.73, respectively, per 100 micromol l(-1) h(-1) increase in AUC(HD-MTX).; Individualised dosing of HD-MTX might have the potential to improve clinical outcome in patients with PCNSL, even when administered concurrently with HD-AraC. In the future, this could be carried out by using first-cycle PK modelling with determination of potential dose adaptations for later cycles using Bayesian analysis

Neo-Atlantis: The Netherlands under a 5-m sea level rise

What could happen to the Netherlands if, in 2030, the sea level starts to rise and eventually, after 100 years, a sea level of 5 m above current level would be reached? This question is addressed by studying literature, by interviewing experts in widely differing fields, and by holding an expert workshop on this question. Although most experts believe that geomorphology and current engineering skills would enable the country to largely maintain its territorial integrity, there are reasons to assume that this is not likely to happen. Social processes that precede important political decisions - such as the growth of the belief in the reality of sea level rise and the framing of such decisions in a proper political context (policy window) - evolve slowly. A flood disaster would speed up the decision-making process. The shared opinion of the experts surveyed is that eventually part of the Netherlands would be abandoned. © 2008 The Author(s)

Interplay Between Time-Temperature-Transformation and the Liquid-Liquid Phase Transition in Water

We study the TIP5P water model proposed by Mahoney and Jorgensen, which is closer to real water than previously-proposed classical pairwise additive potentials. We simulate the model in a wide range of deeply supercooled states and find (i) the existence of a non-monotonic ``nose-shaped'' temperature of maximum density line and a non-reentrant spinodal, (ii) the presence of a low temperature phase transition, (iii) the free evolution of bulk water to ice, and (iv) the time-temperature-transformation curves at different densities.Comment: RevTeX4, 4 pages, 4 eps figure

The Fairness Challenge in Computer Networks

In this paper, the concept of fairness in computer networks is investigated. We motivate the need of examining fairness issues by providing example future application scenarios where fairness support is needed in order to experience sufficient service quality. Fairness definitions from political science and their application to computer networks are described and a state-of-the-art overview of research activities in fairness, from issues such a queue management and tcp-friendliness to issues like fairness in layered multi-rate multicast scenarios, is given. We contribute with this paper to the ongoing research activities by defining the fairness challenge with the purpose of helping direct future investigations to with spots on the map of research in fairness

The politicisation of evaluation: constructing and contesting EU policy performance

Although systematic policy evaluation has been conducted for decades and has been growing strongly within the European Union (EU) institutions and in the member states, it remains largely underexplored in political science literatures. Extant work in political science and public policy typically focuses on elements such as agenda setting, policy shaping, decision making, or implementation rather than evaluation. Although individual pieces of research on evaluation in the EU have started to emerge, most often regarding policy “effectiveness” (one criterion among many in evaluation), a more structured approach is currently missing. This special issue aims to address this gap in political science by focusing on four key focal points: evaluation institutions (including rules and cultures), evaluation actors and interests (including competencies, power, roles and tasks), evaluation design (including research methods and theories, and their impact on policy design and legislation), and finally, evaluation purpose and use (including the relationships between discourse and scientific evidence, political attitudes and strategic use). The special issue considers how each of these elements contributes to an evolving governance system in the EU, where evaluation is playing an increasingly important role in decision making

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

Abstract In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials